Interaction Index and Different Methods for Determining Drug Interaction in Combination Therapy

Synergy of retinoic acid and BH3 mimetics in MYC(N)-driven embryonal nervous system tumours

BACKGROUND

Certain paediatric nervous system malignancies have dismal prognoses. Retinoic acid (RA) is used in neuroblastoma treatment, and preclinical data indicate potential benefit in selected paediatric brain tumour entities. However, limited single-agent efficacy necessitates combination treatment approaches.

METHODS

We performed drug sensitivity profiling of 76 clinically relevant drugs in combination with RA in 16 models (including patient-derived tumouroids) of the most common paediatric nervous system tumours. Drug responses were assessed by viability assays, high-content imaging, and apoptosis assays and RA relevant pathways by RNAseq from treated models and patient samples obtained through the precision oncology programme INFORM (n = 2288). Immunoprecipitation detected BCL-2 family interactions, and zebrafish embryo xenografts were used for in vivo efficacy testing.

RESULTS

Group 3 medulloblastoma (MBG3) and neuroblastoma models were highly sensitive to RA treatment. RA induced differentiation and regulated apoptotic genes. RNAseq analysis revealed high expression of BCL2L1 in MBG3 and BCL2 in neuroblastomas. Co-treatments with RA and BCL-2/XL inhibitor navitoclax synergistically decreased viability at clinically achievable concentrations. The combination of RA with navitoclax disrupted the binding of BIM to BCL-XL in MBG3 and to BCL-2 in neuroblastoma, inducing apoptosis in vitro and in vivo.

CONCLUSIONS

RA treatment primes MBG3 and NB cells for apoptosis, triggered by navitoclax cotreatment.

Nonlinear Regression Modelling: A Primer with Applications and Caveats

Use of nonlinear statistical methods and models are ubiquitous in scientific research. However, these methods may not be fully understood, and as demonstrated here, commonly-reported parameter p-values and confidence intervals may be inaccurate. The gentle introduction to nonlinear regression modelling and comprehensive illustrations given here provides applied researchers with the needed overview and tools to appreciate the nuances and breadth of these important methods. Since these methods build upon topics covered in first and second courses in applied statistics and predictive modelling, the target audience includes practitioners and students alike. To guide practitioners, we summarize, illustrate, develop, and extend nonlinear modelling methods, and underscore caveats of Wald statistics using basic illustrations and give key reasons for preferring likelihood methods. Parameter profiling in multiparameter models and exact or near-exact versus approximate likelihood methods are discussed and curvature measures are connected with the failure of the Wald approximations regularly used in statistical software. The discussion in the main paper has been kept at an introductory level and it can be covered on a first reading; additional details given in the Appendices can be worked through upon further study. The associated online Supplementary Information also provides the data and R computer code which can be easily adapted to aid researchers to fit nonlinear models to their data.

A comparative study of in vitro dose-response estimation under extreme observations

Quantifying drug potency, which requires an accurate estimation of dose-response relationship, is essential for drug development in biomedical research and life sciences. However, the standard estimation procedure of the median-effect equation to describe the dose-response curve is vulnerable to extreme observations in common experimental data. To facilitate appropriate statistical inference, many powerful estimation tools have been developed in R, including various dose-response packages based on the nonlinear least squares method with different optimization strategies. Recently, beta regression-based methods have also been introduced in estimation of the median-effect equation. In theory, they can overcome nonnormality, heteroscedasticity, and asymmetry and accommodate flexible robust frameworks and coefficients penalization. To identify a reliable estimation method(s) to estimate dose-response curves even with extreme observations, we conducted a comparative study to review 14 different tools in R and examine their robustness and efficiency via Monte Carlo simulation under a list of comprehensive scenarios. The simulation results demonstrate that penalized beta regression using the mgcv package outperforms other methods in terms of stable, accurate estimation, and reliable uncertainty quantification.

A mechanistic assessment of the nature of pharmacodynamic drug-drug interaction in vivo and in vitro

Combination pharmacotherapy is becoming increasingly necessary because most diseases are pathophysiologically controlled at the subcellular level by target proteins in a combinatorial manner. We demonstrate the application of the stimulus-response mechanistic model in characterising the drug and physiological properties of pharmacodynamic drug-drug interactions (PDDI) using previously published in vitro and in vivo drug combination experiments. The in vitro experiment tested the effect of a combination of SCH66336 and 4-HPR on the survival of in squamous cell carcinoma cell lines, while the in vivo experiment tested the effect of a combination of cetuximab and cisplatin on tumour growth inhibition in female xenograft mice. The model adequately described both experiments, quantified both system and drug properties and predicted the nature of the PDDI mechanism. Strong baseline signals of 7.35 and 610 units existed in the in vitro and in vivo experiments respectively. An overall synergistic relationship (interaction index = 1.03E-8) was detected in the in vitro experiment. In the in vivo model, the overall interaction index was 70,139.45 implying an antagonistic interaction between the cisplatin and the cetuximab signals.

Study on the effect of a triple cancer treatment of propolis, thermal cycling-hyperthermia, and low-intensity ultrasound on PANC-1 cells

To reduce side effects and enhance treatment efficacy, study on combination therapy for pancreatic cancer, a deadly cancer, has gained much attraction in recent years. In this study, we propose a novel triple treatment combining propolis and two physical stimuli-thermal cycling-hyperthermia (TC-HT) and low-intensity ultrasound (US). The study found that, after the triple treatment, the cell viability of a human cancer cell line PANC-1 decreased to a level 80% less than the control, without affecting the normal pancreatic cells. Another result was excessive accumulation of reactive oxygen species (ROS) after the triple treatment, leading to the amplification of apoptotic pathway through the MAPK family and mitochondrial dysfunction. This study, to the best of our knowledge, is the first attempt to combine TC-HT, US, and a natural compound in cancer treatment. The combination of TC-HT and US also promotes the anticancer effect of the heat-sensitive chemotherapy drug cisplatin on PANC-1 cells. It is expected that optimized parameters for different agents and different types of cancer will expand the methodology on oncological therapy in a safe manner.

Combined Treatment of Dendrosomal-Curcumin and Daunorubicin Synergistically Inhibit Cell Proliferation, Migration and Induce Apoptosis in A549 Lung Cancer Cells

Purpose

Chemotherapy drugs used to treat lung cancer are associated with drug resistance and severe side effects. There have been rising demands for new therapeutic candidates and novel approaches, including combination therapy. Here, we aimed to investigate the combinatorial effect of a dendrosomal formulation of curcumin (DNC) and daunorubicin (DNR) on the A549 lung cancer cell line.

Methods

We performed cytotoxicity, apoptosis, cell migration, colony-formation capacity, and gene expression analysis to interpret the mechanism of action for a combination of DNC and DNR on A549 cells.

Results

Our results revealed that the combination of DNC and DNR could synergistically inhibit the A549 cells' growth. This synergistic cytotoxicity was further approved by flow cytometry, migration assessment, colony-forming capacity and gene expression analysis. DNR combination with DNC resulted in increased apoptosis to necrosis ratio compared to DNR alone. In addition, the migration and colony-forming capacity were at the minimal range when DNC was combined with DNR. Combined treatment decreased the expression level of MDR-1, hTERT and Bcl-2 genes significantly. In addition, the ratio of Bax/Bcl2 gene expression significantly increased. Our analysis by free curcumin, dendrosomes and DNC also showed that dendrosomes do not have any significant cytotoxic effect on the A549 cells, suggesting that this carrier has a high potential for enhancing the curcumin's biological effects.

Conclusion

Our observations suggest that the DNC formulation of curcumin synergistically enhances the antineoplastic effect of DNR on the A549 cell line through the modulation of apoptosis/necrosis ratio, as well as Bax/Bcl2 ratio, MDR-1 and hTERT gene expression.

Combinatorial drug screening on 3D Ewing sarcoma spheroids using droplet-based microfluidics

Culturing and screening cells in microfluidics, particularly in three-dimensional formats, has the potential to impact diverse areas from fundamental biology to cancer precision medicine. Here, we use a platform based on anchored droplets for drug screening. The response of spheroids of Ewing sarcoma (EwS) A673 cells to simultaneous or sequential combinations of etoposide and cisplatin was evaluated. This was done by culturing spheroids of EwS cells inside 500 nL droplets then merging them with secondary droplets containing fluorescent-barcoded drugs at different concentrations. Differences in EwS spheroid growth and viability were measured by microscopy. After drug exposure such measurements enabled estimation of their IC50 values, which were in agreement with values obtained in standard multiwell plates. Then, synergistic drug combination was evaluated. Sequential combination treatment of EwS with etoposide applied 24 h before cisplatin resulted in amplified synergistic effect. As such, droplet-based microfluidics offers the modularity required for evaluation of drug combinations.

Drug Combination Modeling: Methods and Applications in Drug Development

Combination therapies have become increasingly researched and used in the treatment and management of complex diseases due to their ability to increase the chances for better efficacy and decreased toxicity. To evaluate drug combinations in drug development, pharmacokinetic and pharmacodynamic interactions between drugs in combination can be quantified using mathematical models; however, it can be difficult to deduce which models to use and how to use them to aid in clinical trial simulations to simulate the effect of a drug combination. This review paper aims to provide an overview of the various methods used to evaluate combination drug interaction for use in clinical trial development and a practical guideline on how combination modeling can be used in the settings of clinical trials.

D-optimal designs for two-variable logistic regression model with restricted design space

The problem of constructing locally D-optimal designs for two-variable logistic model with no interaction has been studied in many literature. In Kabera, Haines, and Ndlovu (2015), the model is restricted to have positive slopes and negative intercept for the assumptions that the probability of response increases with doses for both drugs and that the probability of response is less than 0.5 at zero dose level of both drugs. The design space mainly discussed is the set [0, ∞) × [0, ∞), while the finite rectangular design space is presented only in scenarios where the results for the unlimited design space are still appropriate. In this paper, we intend to loose these restrictions and discuss the rectangular design spaces for the model where the D-optimal designs can not be obtained. The result can be extended to the models where drugs have negative or opposite effects, or the models with positive intercept, by using translation and reflection in the first quadrant.

Synergistic Effect of DIBOA and Verbascoside from Acanthus mollis Leaf on Tyrosinase Inhibition

Overexpression of melanin contributes to darkening of plant and fruit tissues and skin hyperpigmentation, leading to melasma or age spots. Although melanin biosynthesis is complex and involves several steps, a single enzyme known as tyrosinase is key to regulating this process. The melanogenesis pathway is initiated by oxidation of the starting material l-tyrosine (or l-DOPA) to dopaquinone by tyrosinase; the resulting quinone then serves as a substrate for subsequent steps that eventually lead to production of melanin. Medicinal plants are considered a good source of tyrosinase inhibitors. This study investigated the tyrosinase inhibitory activity of A. mollis leaf extracts and their phytochemicals. Significant activity was verified in the ethanol extract -EEt (IC50 = 1.21 µg/mL). Additionally, a kinetic study showed that this tyrosinase inhibition occurs by DIBOA (2,4-dihydroxy-1,4-benzoxazin-3-one) and verbascoside contribution through a non-competitive reaction mechanism. A synergistic effect on tyrosinase inhibition was observed in the binary combination of the compounds. In conclusion, both EEt and a mixture of two of its phytochemicals can be effective tyrosinase inhibitors and can be used as a bleaching agent for cosmetic formulations in the future.

Statistical methods for assessing drug interactions using observational data

With advances in medicine, many drugs and treatments become available. On the one hand, polydrug use (i.e. using more than one drug at a time) has been used to treat patients with multiple morbid conditions, and polydrug use may cause severe side effects. On the other hand, combination treatments have been successfully developed to treat severe diseases such as cancer and chronic diseases. Observational data, such as electronic health record data, may provide useful information for assessing drug interactions. In this article, we propose using marginal structural models to assess the average treatment effect and causal interaction of two drugs by controlling confounding variables. The causal effect and the interaction of two drugs are assessed using the weighted likelihood approach, with weights being the inverse probability of the treatment assigned. Simulation studies were conducted to examine the performance of the proposed method, which showed that the proposed method was able to estimate the causal parameters consistently. Case studies were conducted to examine the joint effect of metformin and glyburide use on reducing the hospital readmission for type 2 diabetic patients, and to examine the joint effect of antecedent statins and opioids use on the immune and inflammatory biomarkers for COVID-19 hospitalized patients.

KP372-1-Induced AKT Hyperactivation Blocks DNA Repair to Synergize With PARP Inhibitor Rucaparib via Inhibiting FOXO3a/GADD45α Pathway

Poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi) have exhibited great promise in the treatment of tumors with homologous recombination (HR) deficiency, however, PARPi resistance, which ultimately recovers DNA repair and cell progress, has become an enormous clinical challenge. Recently, KP372-1 was identified as a novel potential anticancer agent that targeted the redox enzyme, NAD(P)H:quinone oxidoreductase 1 (NQO1), to induce extensive reactive oxygen species (ROS) generation that amplified DNA damage, leading to cancer cell death. To overcome PARPi resistance and expand its therapeutic utility, we investigated whether a combination therapy of a sublethal dose of KP372-1 with a nontoxic dose of PARPi rucaparib would synergize and enhance lethality in NQO1 over-expressing cancers. We reported that the combination treatment of KP372-1 and rucaparib induced a transient and dramatic AKT hyperactivation that inhibited DNA repair by regulating FOXO3a/GADD45α pathway, which enhanced PARPi lethality and overcame PARPi resistance. We further found that PARP inhibition blocked KP372-1-induced PARP1 hyperactivation to reverse NAD⁺/ATP loss that promoted Ca²⁺-dependent autophagy and apoptosis. Moreover, pretreatment of cells with BAPTA-AM, a cytosolic Ca²⁺ chelator, dramatically rescued KP372-1- or combination treatment-induced lethality and significantly suppressed PAR formation and γH2AX activation. Finally, we demonstrated that this combination therapy enhanced accumulation of both agents in mouse tumor tissues and synergistically suppressed tumor growth in orthotopic pancreatic and non-small-cell lung cancer xenograft models. Together, our study provides novel preclinical evidence for new combination therapy in NQO1⁺ solid tumors that may broaden the clinical utility of PARPi.

Robust and Efficient Assessment of Potency (REAP) as a quantitative tool for dose-response curve estimation

The median-effect equation has been widely used to describe the dose-response relationship and identify compounds that activate or inhibit specific disease targets in contemporary drug discovery. However, the experimental data often contain extreme responses, which may significantly impair the estimation accuracy and impede valid quantitative assessment in the standard estimation procedure. To improve the quantitative estimation of the dose-response relationship, we introduce a novel approach based on robust beta regression. Substantive simulation studies under various scenarios demonstrate solid evidence that the proposed approach consistently provides robust estimation for the median-effect equation, particularly when there are extreme outcome observations. Moreover, simulation studies illustrate that the proposed approach also provides a narrower confidence interval, suggesting a higher power in statistical testing. Finally, to efficiently and conveniently perform common lab data analyses, we develop a freely accessible web-based analytic tool to facilitate the quantitative implementation of the proposed approach for the scientific community.

Synergistic Interactions between Tocol and Phenolic Extracts from Different Tree Nut Species against Human Cancer Cell Lines

Tree nuts are rich in polar (phenolic compounds) and non-polar (tocols) antioxidants, with recognized effects in the prevention of diseases such as cancer. These biomolecules possess antiproliferative activity on cancer cells; however, the combined effect of both types of compounds has been scarcely studied, and this approach could give valuable information on the real anticancer potential of tree nuts. In the present study, the antiproliferative activity of pure tocols and phenolic compounds, tocol- and phenolic-rich extracts (TRE and PRE, respectively) from tree nuts and the extracts combinations, was evaluated in four cancer (HeLa, MCF7, PC3, A549) and one control (ARPE) cell lines. The most sensible cell lines were HeLa and MCF7. TRE and PRE from nuts were chemically characterized; γ and δ tocopherols, total tocols, total tocopherols and total phenolic compounds were negatively correlated with cell viability in MCF7 cells. In HeLa cells, only δ and total tocopherols were negatively correlated with cell viability. TRE and PRE had a low effect in reducing cell viability of the cancer cell lines, the most effective extracts were those of emory oak acorn (EOA), pecan nut (PEC) and walnut (WAL), and these were further studied for their pharmacological interactions, using the combination index and the isobologram methods. Combinations of both extracts showed a synergistic and strongly synergistic behavior in the three nuts (EOA, PEC and WAL), with combination indexes between 0.12 and 0.55. These results highlight the need to understand the interactions among components found in complex natural extracts or food products in order to fully understand their bioactivities.

In Vitro Evaluation of the Individual and Combined Cytotoxic and Estrogenic Effects of Zearalenone, Its Reduced Metabolites, Alternariol, and Genistein

Mycotoxins are toxic metabolites of filamentous fungi. Previous studies demonstrated the co-occurrence of Fusarium and Alternaria toxins, including zearalenone (ZEN), ZEN metabolites, and alternariol (AOH). These xenoestrogenic mycotoxins appear in soy-based meals and dietary supplements, resulting in the co-exposure to ZEN and AOH with the phytoestrogen genistein (GEN). In this study, the cytotoxic and estrogenic effects of ZEN, reduced ZEN metabolites, AOH, and GEN are examined to evaluate their individual and combined impacts. Our results demonstrate that reduced ZEN metabolites, AOH, and GEN can aggravate ZEN-induced toxicity; in addition, the compounds tested exerted mostly synergism or additive combined effects regarding cytotoxicity and/or estrogenicity. Therefore, these observations underline the importance and the considerable risk of mycotoxin co-exposure and the combined effects of mycoestrogens with phytoestrogens.

Anti-Angiogenic Properties of Ginsenoside Rg3 Epimers: In Vitro Assessment of Single and Combination Treatments

Tumour angiogenesis plays a key role in tumour growth and progression. The application of current anti-angiogenic drugs is accompanied by adverse effects and drug resistance. Therefore, finding safer effective treatments is needed. Ginsenoside Rg3 (Rg3) has two epimers, 20(S)-Rg3 (SRg3) and 20(R)-Rg3 (RRg3), with stereoselective activities. Using response surface methodology, we optimised a combination of these two epimers for the loop formation of human umbilical vein endothelial cell (HUVEC). The optimised combination (C3) was tested on HUVEC and two murine endothelial cell lines. C3 significantly inhibited the loop formation, migration, and proliferation of these cells, inducing apoptosis in HUVEC and cell cycle arrest in all of the cell lines tested. Using molecular docking and vascular endothelial growth factor (VEGF) bioassay, we showed that Rg3 has an allosteric modulatory effect on vascular endothelial growth factor receptor 2 (VEGFR2). C3 also decreased the VEGF expression in hypoxic conditions, decreased the expression of aquaporin 1 and affected AKT signaling. The proteins that were mostly affected after C3 treatment were those related to mammalian target of rapamycin (mTOR). Eukaryotic translation initiation factor 4E (eIF4E)-binding protein 1 (4E-BP1) was one of the important targets of C3, which was affected in both hypoxic and normoxic conditions. In conclusion, these results show the potential of C3 as a novel anti-angiogenic drug.

HER3-Receptor-Mediated STAT3 Activation Plays a Central Role in Adaptive Resistance toward Vemurafenib in Melanoma

Simple Summary

The major obstacle for the long-term success of targeted therapies in melanoma is the occurrence of resistance. Here, we present a new mechanism of targeted therapy resistance in melanoma where the treatment with the BRAF inhibitor vemurafenib causes an increased activation of HER3 via shed ligands. This is followed by an activation of STAT3 via HER3 and results in the expression of the STAT3 target gene SOX2. Pharmacological inhibition of HERs sensitizes melanoma cells toward vemurafenib treatment. Thus, blocking HER family members and especially HER3 in addition to targeted therapy treatment might prevent the occurrence of resistance.

Abstract

Melanoma is an aggressive form of skin cancer that is often characterized by activating mutations in the Mitogen-Activated Protein (MAP) kinase pathway, causing hyperproliferation of the cancer cells. Thus, inhibitors targeting this pathway were developed. These inhibitors are initially very effective, but the occurrence of resistance eventually leads to a failure of the therapy and is the major obstacle for clinical success. Therefore, investigating the mechanisms causing resistance and discovering ways to overcome them is essential for the success of therapy. Here, we observed that treatment of melanoma cells with the B-Raf Proto-Oncogene, Serine/Threonine Kinase (BRAF) inhibitor vemurafenib caused an increased cell surface expression and activation of human epidermal growth factor receptor 3 (HER3) by shed ligands. HER3 promoted the activation of signal transducer and activator of transcription 3 (STAT3) resulting in upregulation of the STAT3 target gene SRY-Box Transcription Factor 2 (SOX2) and survival of the cancer cells. Pharmacological blocking of HER led to a diminished STAT3 activation and increased sensitivity toward vemurafenib. Moreover, HER blocking sensitized vemurafenib-resistant cells to drug treatment. We conclude that the inhibition of the STAT3 upstream regulator HER might help to overcome melanoma therapy resistance toward targeted therapies.

An R-shiny application to calculate optimal designs for single substance and interaction trials in dose response experiments

BACKGROUND AND OBJECTIVE

Optimal experimental design theory proposes choosing specific settings in experimental trials in order to maximize the precision of the resulting parameter estimates. In dose response experiments, this corresponds to choosing the optimal dose levels for every available observation, and can be applied both to singular dose-response relationships and to interaction experiments where two substances are given simultaneously at several different mixture ratios ("ray designs"). While the theory of experimental design for this situation is well developed, the mathematical complexity prevents widespread use in practical applications. A simple to use application making the theory accessible to practitioners is thus very desirable.

METHODS

Results from established optimal experimental design theory are applied to dose response applications, focusing on log-logistic and Weibull class dose response functions. Suitable optimal design algorithms to solve these problems are implemented into an R-shiny based online application.

RESULTS

The application provides an interface to easily calculate D-optimal designs not only for singular dose experiments, but also for interaction trials with several combination rays of substances. Furthermore, the app also allows evaluating the efficiency of existing candidate designs, and finally allows construction of designs which perform robustly under different assumptions in regard to the true parameters.

Chitosan-Based Microparticles Enhance Ellagic Acid's Colon Targeting and Proapoptotic Activity

This study aimed at improving the targeting and cytotoxic effect of ellagic acid (EA) on colon cancer cells. EA was encapsulated in chitosan (CHIT) polymers then coated by eudragit S100 (ES100) microparticles. The release of EA double-coated microparticles (MPs) was tested at simulative pH values. Maximum release was observed at 24 h and pH 7.4. The cytotoxicity of EA MPs on HCT 116 colon cancer cells was synergistically improved as compared with raw EA. Cell-cycle analysis by flow cytometry suggested enhanced G2-M phase colon cancer cell accumulation. In addition, a significantly higher cell fraction was observed in the pre-G phase, which highlighted the enhancement of the proapoptotic activity of EA formulated in the double-coat mixture. Annexin-V staining was used for substantiation of the observed cell-death-inducing activity. Cell fractions were significantly increased in early, late, and total cell death. This was backed by high elevation in cellular content of caspase 3. Effectiveness of the double-coated EA to target colonic tissues was confirmed using real-time iohexol dye X-ray radiography. In conclusion, CHIT loaded with EA and coated with ES100 formula exhibits improved colon targeting as well as enhanced cytotoxic and proapoptotic activity against HCT 116 colon cancer when compared with the administration of raw EA.

Application of non-invasive low-intensity pulsed electric field with thermal cycling-hyperthermia for synergistically enhanced anticancer effect of chlorogenic acid on PANC-1 cells

Most existing cancer treatments involve high-cost chemotherapy and radiotherapy, with major side effects, prompting effort to develop alternative treatment modalities. It was reported that the combination of thermal-cycling hyperthermia (TC-HT) and phenolic compound exhibited a moderate cytotoxic effect against human pancreatic cancer PANC-1 cells. In this study, we investigate the efficacy of triple combination in PANC-1 cancer cells by adopting low-intensity pulsed electric field (LIPEF) to couple with TC-HT and CGA (chlorogenic acid). The study finds that this triple combination can significantly impede the proliferation of PANC-1 cells, with only about 20% viable cells left after 24h, whereas being non-toxic to normal cells. The synergistic activity against the PANC-1 cells was achieved by inducing G2/M phase arrest and apoptosis, which were associated with up-regulation of p53 and coupled with increased expression of downstream proteins p21 and Bax. Further mechanism investigations revealed that the cytotoxic activity could be related to mitochondrial apoptosis, characterized by the reduced level of Bcl-2, mitochondrial dysfunction, and sequential activation of caspase-9 and PARP. Also, we found that the triple treatment led to the increase of intracellular reactive oxygen species (ROS) production. Notably, the triple treatment-induced cytotoxic effects and the elevated expression of p53 and p21 proteins as well as the increased Bax/Bcl-2 ratio, all could be alleviated by the ROS scavenger, N-acetyl-cysteine (NAC). These findings indicate that the combination of CGA, TC-HT, and LIPEF may be a promising modality for cancer treatment, as it can induce p53-dependent cell cycle arrest and apoptosis through accumulation of ROS in PANC-1 cells.

Utilizing Liposomal Quercetin and Gallic Acid in Localized Treatment of Vaginal Candida Infections

Vulvovaginal candidiasis (VVC) is a widely spread fungal infection that causes itching, pain and inflammation at the vaginal site. Although common, currently available treatment suffers from limited efficacy and high recurrence. In addition, the growing problem of resistance to azole drugs used in current treatments emphasizes the need for superior treatment options. Antimicrobial polyphenols are an attractive approach offering multitargeting therapy. We aimed to develop novel liposomes for simultaneous delivery of two polyphenols (quercetin, Q, and gallic acid, GA) that, when released within the vaginal cavity, act in synergy to eradicate infection while alleviating the symptoms of VVC. Q was selected for its anti-itching and anti-inflammatory properties, while GA for its reported activity against Candida. Novel liposomes containing only Q (LP-Q), only GA (LP-GA) or both polyphenols (LP-Q+GA) were in the size range around 200 nm. Q was efficiently entrapped in both LP-Q and in LP-Q+GA (85%) while the entrapment of GA was higher in LP-Q+GA (30%) than in LP-GA (25%). Liposomes, especially LP-Q+GA, promoted sustained release of both polyphenols. Q and GA acted in synergy, increasing the antioxidant activities of a single polyphenol. Polyphenol-liposomes were not cytotoxic and displayed stronger anti-inflammatory effects than free polyphenols. Finally, LP-GA and LP-Q+GA considerably reduced C. albicans growth.

Modeling dose-response functions for combination treatments with log-logistic or Weibull functions

Dose-response curves of new substances in toxicology and related areas are commonly fitted using log-logistic functions. In more advanced studies, an additional interest is often how these substances will behave when applied in combination with a second substance. Here, an essential question for both design and analysis of these combination experiments is whether the resulting dose-response function will still be a member of the class of log-logistic functions, and, if so, what function parameters will result for the combined substances. Different scenarios might be considered in regard to whether a true interaction between the substances is expected, or whether the combination will simply be additive. In this paper, it is shown that the resulting function will in general not be a log-logistic function, but can be approximated very closely with one. Parameters for this approximation can be predicted from the parameters of both ingredients. Furthermore, some simple interaction structures can still be represented with a single log-logistic function. The approach can also be applied to Weibull-type dose-response functions, and similar results are obtained. Finally, the results were applied to a real data set obtained from cell culture experiments involving two cancer treatments, and the dose-response curve of a combination treatment was predicted from the properties of the singular substances.

Central nervous system evaluation of an ethanol extract of Bidens odorata Cav (Asteraceae) leaves, and its antinociceptive interaction with paracetamol and naproxen

INTRODUCTION

Bidens odorata Cav (Asteraceae) is a medicinal plant employed for the treatment of pain, anxiety, and depression. This study aimed to evaluate some neuropharmacological effects of an ethanol extract of B. odorata (BOE) and assess its antinociceptive interaction with naproxen and paracetamol.

MATERIALS AND METHODS

The following neuropharmacological effects were evaluated with the ethanolic extract of B. odorata leaves (BOE) (10-200 mg/kg p.o.): the strychnine-induced-convulsion assay (anticonvulsant effect), rotarod test (locomotor activity), tail suspension test (anti-depressant-like activity), cylinder exploratory test (anxiolytic-like actions), and pentobarbital-induced sleep test (sedative effect). The interaction of the BOE-paracetamol and BOE-naproxen combinations were evaluated with the acetic acid-induced writhing test. The ED₅₀ value of each drug was estimated and the combinations of paracetamol and naproxen with BOE were calculated.

RESULTS

BOE (100-200 mg/kg) showed anti-convulsant activity by increasing the latency to occurrence of strychnine-induced convulsions, antidepressant-like effects by 28% and 33%, respectively, exerted anxiolytic actions (ED₅₀ = 125 mg/kg), but did not affect motor locomotion. The pre-treatment with 2 mg/kg flumazenil or 20 mg/kg pentylenetetrazol partially reverted the anxiolytic activity shown by BOE alone. BOE (200 mg/kg) prolonged the duration of sleep with similar effect in comparison to clonazepam (1.5 mg/kg). The combinations of BOE-paracetamol (1:1) and BOE-naproxen (1:1) showed antinociceptive synergism.

CONCLUSION

BOE induces sedative and anticonvulsant effects. The anxiolytic actions shown by BOE are probably induced by the participation of the GABAergic system. BOE exerts antinociceptive synergistic interaction with paracetamol and naproxen probably by the participation of nitric oxide and ATP-sensitive K⁺ channels, respectively.

Synergy and antagonism in natural product extracts: when 1 + 1 does not equal 2

Covering: 2000 to 2019 According to a 2012 survey from the Centers for Disease Control and Prevention, approximately 18% of the U.S. population uses natural products (including plant-based or botanical preparations) for treatment or prevention of disease. The use of plant-based medicines is even more prevalent in developing countries, where for many they constitute the primary health care modality. Proponents of the medicinal use of natural product mixtures often claim that they are more effective than purified compounds due to beneficial "synergistic" interactions. A less-discussed phenomenon, antagonism, in which effects of active constituents are masked by other compounds in a complex mixture, also occurs in natural product mixtures. Synergy and antagonism are notoriously difficult to study in a rigorous fashion, particularly given that natural products chemistry research methodology is typically devoted to reducing complexity and identifying single active constituents for drug development. This report represents a critical review with commentary about the current state of the scientific literature as it relates to studying combination effects (including both synergy and antagonism) in natural product extracts. We provide particular emphasis on analytical and Big Data approaches for identifying synergistic or antagonistic combinations and elucidating the mechanisms that underlie their interactions. Specific case studies of botanicals in which synergistic interactions have been documented are also discussed. The topic of synergy is important given that consumer use of botanical natural products and associated safety concerns continue to garner attention by the public and the media. Guidance by the natural products community is needed to provide strategies for effective evaluation of safety and toxicity of botanical mixtures and to drive discovery in botanical natural product research.

Drug Combinations: Mathematical Modeling and Networking Methods

Treatments consisting of mixtures of pharmacological agents have been shown to have superior effects to treatments involving single compounds. Given the vast amount of possible combinations involving multiple drugs and the restrictions in time and resources required to test all such combinations in vitro, mathematical methods are essential to model the interactive behavior of the drug mixture and the target, ultimately allowing one to better predict the outcome of the combination. In this review, we investigate various mathematical methods that model combination therapies. This survey includes the methods that focus on predicting the outcome of drug combinations with respect to synergism and antagonism, as well as the methods that explore the dynamics of combination therapy and its role in combating drug resistance. This comprehensive investigation of the mathematical methods includes models that employ pharmacodynamics equations, those that rely on signaling and how the underlying chemical networks are affected by the topological structure of the target proteins, and models that are based on stochastic models for evolutionary dynamics. Additionally, this article reviews computational methods including mathematical algorithms, machine learning, and search algorithms that can identify promising combinations of drug compounds. A description of existing data and software resources is provided that can support investigations in drug combination therapies. Finally, the article concludes with a summary of future directions for investigation by the research community.

Predictive approaches for drug combination discovery in cancer

Drug combinations have been proposed as a promising therapeutic strategy to overcome drug resistance and improve efficacy of monotherapy regimens in cancer. This strategy aims at targeting multiple components of this complex disease. Despite the increasing number of drug combinations in use, many of them were empirically found in the clinic, and the molecular mechanisms underlying these drug combinations are often unclear. These challenges call for rational, systematic approaches for drug combination discovery. Although high-throughput screening of single-agent therapeutics has been successfully implemented, it is not feasible to test all possible drug combinations, even for a reduced subset of anticancer drugs. Hence, in vitro and in vivo screening of a large number of drug combinations are not practical. Therefore, devising computational methods to efficiently explore the space of drug combinations and to discover efficacious combinations has attracted a lot of attention from the scientific community in the past few years. Nevertheless, in the absence of consensus regarding the computational approaches used to predict efficacious drug combinations, a plethora of methods, techniques and hypotheses have been developed to date, while the research field lacks an elaborate categorization of the existing computational methods and the available data sources. In this manuscript, we review and categorize the state-of-the-art computational approaches for drug combination prediction, and elaborate on the limitations of these methods and the existing challenges. We also discuss about the recent pan-cancer drug combination data sets and their importance in revising the available methods or developing more performant approaches.

A Novel MCL1 Inhibitor Combined with Venetoclax Rescues Venetoclax-Resistant Acute Myelogenous Leukemia

Suppression of apoptosis by expression of antiapoptotic BCL2 family members is a hallmark of acute myeloblastic leukemia (AML). Induced myeloid leukemia cell differentiation protein (MCL1), an antiapoptotic BCL2 family member, is commonly upregulated in AML cells and is often a primary mode of resistance to treatment with the BCL2 inhibitor venetoclax. Here, we describe VU661013, a novel, potent, selective MCL1 inhibitor that destabilizes BIM/MCL1 association, leads to apoptosis in AML, and is active in venetoclax-resistant cells and patient-derived xenografts. In addition, VU661013 was safely combined with venetoclax for synergy in murine models of AML. Importantly, BH3 profiling of patient samples and drug-sensitivity testing ex vivo accurately predicted cellular responses to selective inhibitors of MCL1 or BCL2 and showed benefit of the combination. Taken together, these data suggest a strategy of rationally using BCL2 and MCL1 inhibitors in sequence or in combination in AML clinical trials. SIGNIFICANCE: Targeting antiapoptotic proteins in AML is a key therapeutic strategy, and MCL1 is a critical antiapoptotic oncoprotein. Armed with novel MCL1 inhibitors and the potent BCL2 inhibitor venetoclax, it may be possible to selectively induce apoptosis by combining or thoughtfully sequencing these inhibitors based on a rational evaluation of AML.See related commentary by Leber et al., p. 1511.This article is highlighted in the In This Issue feature, p. 1494.

The BET Bromodomain Inhibitor OTX015 Synergizes with Targeted Agents in Multiple Myeloma

Treatment failure remains a main challenge in the management of high-risk multiple myeloma (MM) even with the expanding repertoire of new drugs. Combinatorial therapy is considered an encouraging strategy that can overcome the compensatory mechanisms and undesirable off-target effects that limit the benefits of many prospective agents. Preliminary results of a current phase I trial have indicated that the new BET bromodomain inhibitor OTX015 has favorable activity and tolerability. However, OTX015 is not efficacious enough as a monotherapy. Here, we provide evidence that synergistic drug combinations with OTX015 were generally more specific to particular cellular contexts than single agent activities. In addition, pairing OTX015 with three classes of drugs dramatically enhanced the antitumor activity in mouse models of disseminated human myeloma. Our studies further underscored that the BET inhibitor OTX015 sensitized MM cells by interrupting several pathways and genes critical for MM cell proliferation and drug response, which provided the rationale for multiple myeloma therapy with OTX015 combined with conventional chemotherapeutic drugs. Thus, the context specificity of synergistic combinations not only provide profound insights into therapeutically relevant selectivity but also improve control of complex biological systems.

Repurposing of Bromocriptine for Cancer Therapy

Bromocriptine is an ergot alkaloid and dopamine D₂ receptor agonist used to treat Parkinson's disease, acromegaly, hyperprolactinemia, and galactorrhea, and more recently diabetes mellitus. The drug is also active against pituitary hormone-dependent tumors (prolactinomas and growth-hormone producing adenomas). We investigated, whether bromocriptine also inhibits hormone-independent and multidrug-resistant (MDR) tumors. We found that bromocriptine was cytotoxic towards drug-sensitive CCRF-CEM, multidrug-resistant CEM/ADR5000 leukemic cells as well as wild-type or multidrug-resistant ABCB5-transfected HEK293 cell lines, but not sensitive or BCRP-transfected multidrug-resistant MDA-MB-231 breast cancer cells. Bromocriptine strongly bound to NF-κB pathway proteins as shown by molecular docking and interacted more strongly with DNA-bound NF-κB than free NF-κB, indicating that bromocriptine may inhibit NF-κB binding to DNA. Furthermore, bromocriptine decreased NF-κB activity by a SEAP-driven NF-κB reporter cell assay. The expression of MDR-conferring ABC-transporters (ABCB1, ABCB5, ABCC1, and ABCG2) and other resistance-mediating factors (EGFR, mutated TP53, and IκB) did not correlate with cellular response to bromocriptine in a panel of 60 NCI cell lines. There was no correlation between cellular response to bromocriptine and anticancer drugs usually involved in MDR (e.g., anthracyclines, Vinca alkaloids, taxanes, epipodophyllotoxins, and others). COMPARE analysis of microarray-based mRNA expression in these cell lines revealed that genes from various functional groups such as ribosomal proteins, transcription, translation, DNA repair, DNA damage, protein folding, mitochondrial respiratory chain, and chemokines correlated with cellular response to bromocriptine. Our results indicate that bromocriptine inhibited drug-resistant tumor cells with different resistance mechanisms in a hormone-independent manner. As refractory and otherwise drug-resistant tumors represent a major challenge to successful cancer chemotherapy, bromocriptine may be considered for repurposing in cancer therapy.

Searching Synergistic Dose Combinations for Anticancer Drugs

Recent development has enabled synergistic drugs in treating a wide range of cancers. Being highly context-dependent, however, identification of successful ones often requires screening of combinational dose on different testing platforms in order to gain the best anticancer effects. To facilitate the development of effective computational models, we reviewed the latest strategy in searching optimal dose combination from three perspectives: (1) mainly experimental-based approach; (2) Computational-guided experimental approach; and (3) mainly computational-based approach. In addition to the introduction of each strategy, critical discussion of their advantages and disadvantages were also included, with a strong focus on the current applications and future improvements.

Fucoxanthin Elicits Epigenetic Modifications, Nrf2 Activation and Blocking Transformation in Mouse Skin JB6 P+ Cells

Nuclear factor erythroid-2-related factor-2 (Nrf2 or NFE2L2) is a master regulator of the anti-oxidative stress response, which is involved in the defense against many oxidative stress/inflammation-mediated diseases, including anticancer effects elicited by an increasing number of natural products. Our previous studies showed that the epigenetic modification of the Nrf2 gene plays a key role in restoring the expression of Nrf2. In this study, we aimed to investigate the epigenetic regulation of Nrf2 by astaxanthin (AST) and fucoxanthin (FX), carotenoids which are abundant in microalgae and seaweeds, in mouse skin epidermal JB6 P+ cells. FX induced the anti-oxidant response element (ARE)-luciferase and upregulated the mRNA and protein levels of Nrf2 and Nrf2 downstream genes in HepG2-C8 cells overexpressing the ARE-luciferase reporter. Both FX and AST decreased colony formation in 12-Otetradecanoylphorbol-13-acetate (TPA)-induced transformation of JB6 P+ cells. FX decreased the methylation of the Nrf2 promoter region in the JB6 P+ cells by the bisulfite conversion and pyrosequencing. Both FX and AST significantly reduced DNA methyltransferase (DNMT) activity but did not affect histone deacetylase (HDAC) activity in JB6 P+ cells. In summary, our results show that FX activates the Nrf2 signaling pathway, induces the epigenetic demethylation of CpG sites in Nrf2 and blocks the TPA-induced transformation of JB6 P+ cells, indicating the potential health-promoting effects of FX in skin cancer prevention.

Evaluation of drug combination effect using a Bliss independence dose-response surface model

To test the anticancer effect of combining two drugs targeting different biological pathways, the popular way to show synergistic effect of drug combination is a heat map or surface plot based on the percent excess the Bliss prediction using the average response measures at each combination dose. Such graphs, however, are inefficient in the drug screening process and it doesn't give a statistical inference on synergistic effect. To make a statistically rigorous and robust conclusion for drug combination effect, we present a two-stage Bliss independence response surface model to estimate an overall interaction index (τ) with 95% confidence interval (CI). By taking into all data points account, the overall τ with 95% CI can be applied to determine if the drug combination effect is synergistic overall. Using some example data, the two-stage model was compared to a couple of classic models following Bliss rule. The data analysis results obtained from our model reflect the pattern shown from other models. The application of overall τ helps investigators to make decision easier and accelerate the preclinical drug screening.

A general pharmacodynamic interaction model identifies perpetrators and victims in drug interactions

Assessment of pharmacodynamic (PD) drug interactions is a cornerstone of the development of combination drug therapies. To guide this venture, we derive a general pharmacodynamic interaction (GPDI) model for ≥2 interacting drugs that is compatible with common additivity criteria. We propose a PD interaction to be quantifiable as multidirectional shifts in drug efficacy or potency and explicate the drugs’ role as victim, perpetrator or even both at the same time. We evaluate the GPDI model against conventional approaches in a data set of 200 combination experiments in Saccharomyces cerevisiae: 22% interact additively, a minority of the interactions (11%) are bidirectional antagonistic or synergistic, whereas the majority (67%) are monodirectional, i.e., asymmetric with distinct perpetrators and victims, which is not classifiable by conventional methods. The GPDI model excellently reflects the observed interaction data, and hence represents an attractive approach for quantitative assessment of novel combination therapies along the drug development process.

Assessment of pharmacodynamic interactions is at the heart of combination therapy development. Here the authors introduce a general drug interaction scoring model that enables quantification of synergistic and antagonistic interactions and determination of the directionality of the interactions.

Developing an Agent-Based Drug Model to Investigate the Synergistic Effects of Drug Combinations

The growth and survival of cancer cells are greatly related to their surrounding microenvironment. To understand the regulation under the impact of anti-cancer drugs and their synergistic effects, we have developed a multiscale agent-based model that can investigate the synergistic effects of drug combinations with three innovations. First, it explores the synergistic effects of drug combinations in a huge dose combinational space at the cell line level. Second, it can simulate the interaction between cells and their microenvironment. Third, it employs both local and global optimization algorithms to train the key parameters and validate the predictive power of the model by using experimental data. The research results indicate that our multicellular system can not only describe the interactions between the microenvironment and cells in detail, but also predict the synergistic effects of drug combinations.

Parametric modeling and optimal experimental designs for estimating isobolograms for drug interactions in toxicology

In toxicology and related areas, interaction effects between two substances are commonly expressed through a combination index [Formula: see text] evaluated separately at different effect levels and mixture ratios. Often, these indices are combined into a graphical representation, the isobologram. Instead of estimating the combination indices at the experimental mixture ratios only, we propose a simple parametric model for estimating the underlying interaction function. We integrate this approach into a joint model where both the parameters of the dose-response functions of the singular substances and the interaction parameters can be estimated simultaneously. As an additional benefit, this concept allows to determine optimal statistical designs for combination studies optimizing the estimation of the interaction function as a whole. From an optimal design perspective, finding the interaction parameters generally corresponds to a [Formula: see text]-optimality resp. [Formula: see text]-optimality design problem, while estimation of all underlying dose response parameters corresponds to a [Formula: see text]-optimality design problem. We show how optimal designs can be obtained in either case as well as how combination designs providing reasonable performance in regard to both criteria can be determined by putting a constraint on the efficiency in regard to one of the criteria and optimizing for the other. As all designs require prior information about model parameter values, which may be unreliable in practice, the effect of misspecifications is investigated as well.

Inhibition of exportin-1 function results in rapid cell cycle-associated DNA damage in cancer cells

Selective inhibitors of nuclear export (SINE) are small molecules in development as anti-cancer agents. The first-in-class SINE, selinexor, is in clinical trials for blood and solid cancers. Selinexor forms a covalent bond with exportin-1 at cysteine-528, and blocks its ability to export cargos. Previous work has shown strong cell cycle effects and drug-induced cell death across many different cancer-derived cell lines. Here, we report strong cell cycle-associated DNA double-stranded break formation upon the treatment of cancer cells with SINE. In multiple cell models, selinexor treatment results in the formation of clustered DNA damage foci in 30-40% of cells within 8 hours that is dependent upon cysteine-528. DNA damage strongly correlates with G1/S-phase and decreased DNA replication. Live cell microscopy reveals an association between DNA damage and cell fate. Cells that form damage in G1-phase more often die or arrest, while those damaged in S/G2-phase frequently progress to cell division. Up to half of all treated cells form damage foci, and most cells that die after being damaged, were damaged in G1-phase. By comparison, non-transformed cell lines show strong cell cycle effects but little DNA damage and less death than cancer cells. Significant drug combination effects occur when selinexor is paired with different classes of agents that either cause DNA damage or that diminish DNA damage repair. These data present a novel effect of exportin-1 inhibition and provide a strong rationale for multiple combination treatments of selinexor with agents that are currently in use for the treatment of different solid cancers.

Venetoclax Synergizes with Radiotherapy for Treatment of B-cell Lymphomas

Constitutive B-cell receptor signaling leads to overexpression of the antiapoptotic BCL-2 protein and is implicated in the pathogenesis of many types of B-cell non-Hodgkin lymphoma (B-NHL). The BCL-2 small-molecule inhibitor venetoclax shows promising clinical response rates in several lymphomas, but is not curative as monotherapy. Radiotherapy is a rational candidate for combining with BCL-2 inhibition, as DNA damage caused by radiotherapy increases the activity of pro-apoptotic BCL-2 pathway proteins, and lymphomas are exquisitely sensitive to radiation. We tested B-NHL responses to venetoclax combined with either external beam radiotherapy or radioimmunotherapy (RIT), which joins the selectivity of antibody targeting with the effectiveness of irradiation. We first tested cytotoxicity of cesium-137 irradiation plus venetoclax in 14 B-NHL cell lines representing five lymphoma subtypes. Combination treatment synergistically increased cell death in 10 of 14 lines. Lack of synergy was predicted by resistance to single-agent venetoclax and high BCL-XL expression. We then assessed the efficacy of external beam radiotherapy plus venetoclax in murine xenograft models of mantle cell (MCL), germinal-center diffuse large B-cell (GCB-DLBCL), and activated B-cell (ABC-DLBCL) lymphomas. In each model, external beam radiotherapy plus venetoclax synergistically increased mouse survival time, curing up to 10%. We finally combined venetoclax treatment of MCL and ABC-DLBCL xenografts with a pretargeted RIT (PRIT) system directed against the CD20 antigen. Optimal dosing of PRIT plus venetoclax cured 100% of mice with no detectable toxicity. Venetoclax combined with radiotherapy may be a promising treatment for a wide range of lymphomas Cancer Res; 77(14); 3885-93. ©2017 AACR.

Monotonic single-index models to assess drug interactions

Although single-index models have been extensively studied, the monotonicity of the link function f in the single-index model is rarely studied. In many situations, it is desirable that f is monotonic, which results in a monotonic single-index model that can be very useful in economics and biometrics. In this article, we propose a monotonic single-index model in which the link function is constructed using penalized I-splines along with constraints on coefficients to achieve monotonicity of the link function f. An algorithm to estimate the single-index parameters and the link function is developed, and the sandwich estimate of the variance of the index parameters is provided. We propose to apply this monotonic single-index model to estimate the dose-response surface and assess drug interactions while considering the variability of the observed data. An extensive simulation study was carried out to evaluate the performance of the proposed monotonic single-index model. A case study is provided to illustrate the application of the proposed model to estimate the dose-response surface and assess drug interactions. Both the simulation and case study show that the proposed monotonic single-index model works very well. Copyright © 2016 John Wiley & Sons, Ltd.

The Influence of Cellular Characteristics on the Evolution of Shape Homeostasis

The importance of individual cells in a developing multicellular organism is well known, but precisely how the individual cellular characteristics of those cells collectively drive the emergence of robust, homeostatic structures is less well understood. For example, cell communication via a diffusible factor allows for information to travel across large distances within the population, and cell polarization makes it possible to form structures with a particular orientation, but how do these processes interact to produce a more robust and regulated structure? In this study we investigate the ability of cells with different cellular characteristics to grow and maintain homeostatic structures. We do this in the context of an individual-based model where cell behavior is driven by an intracellular network that determines the cell phenotype. More precisely, we investigated evolution with 96 different permutations of our model, where cell motility, cell death, long-range growth factor (LGF), short-range growth factor (SGF), and cell polarization were either present or absent. The results show that LGF has the largest positive influence on the fitness of the evolved solutions. SGF and polarization also contribute, but all other capabilities essentially increase the search space, effectively making it more difficult to achieve a solution. By perturbing the evolved solutions, we found that they are highly robust to both mutations and wounding. In addition, we observed that by evolving solutions in more unstable environments they produce structures that were more robust and adaptive. In conclusion, our results suggest that robust collective behavior is most likely to evolve when cells are endowed with long-range communication, cell polarisation, and selection pressure from an unstable environment.

Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations

Making cancer treatment more effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We urgently need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. The book chapter focuses on mathematical and computational tools to facilitate the discovery of the most promising drug combinations to improve efficacy and prevent resistance. Data integration approaches that leverage drug-target interactions, cancer molecular features, and signaling pathways for predicting, understanding, and testing drug combinations are critically reviewed.

Leveraging an NQO1 Bioactivatable Drug for Tumor-Selective Use of Poly(ADP-ribose) Polymerase Inhibitors

Therapeutic drugs that block DNA repair, including poly(ADP-ribose) polymerase (PARP) inhibitors, fail due to lack of tumor-selectivity. When PARP inhibitors and β-lapachone are combined, synergistic antitumor activity results from sustained NAD(P)H levels that refuel NQO1-dependent futile redox drug recycling. Significant oxygen-consumption-rate/reactive oxygen species cause dramatic DNA lesion increases that are not repaired due to PARP inhibition. In NQO1⁺ cancers, such as non-small-cell lung, pancreatic, and breast cancers, cell death mechanism switches from PARP1 hyperactivation-mediated programmed necrosis with β-lapachone monotherapy to synergistic tumor-selective, caspase-dependent apoptosis with PARP inhibitors and β-lapachone. Synergistic antitumor efficacy and prolonged survival were noted in human orthotopic pancreatic and non-small-cell lung xenograft models, expanding use and efficacy of PARP inhibitors for human cancer therapy.

Epigenetic CpG Methylation of the Promoter and Reactivation of the Expression of GSTP1 by Astaxanthin in Human Prostate LNCaP Cells

Astaxanthin (AST), a red dietary carotenoid, has synergistic antioxidant effects with polyunsaturated fatty acids at low concentrations via Nuclear factor (erythroid-derived 2)-like 2 (NFE2L2 or Nrf2)/antioxidant response element (ARE) signaling. In addition, chromatin remodeling and DNA methylation-based gene silencing represent a common mechanism in prostate carcinogenesis and tumor progression from normal cells to pre-initiated cells and ultimately to invasive carcinoma. Therefore, the control of epigenetic modification and the transcriptional/translational control of the activation of Nrf2 and Nrf2-target genes, including glutathione S-transferases (GSTs), appear to be an important mechanism that protects cells against injuries from oxidative stress and cancer development. In this study, we aim to investigate the role of AST in reactivating the expression of Nrf2 and GSTP1 through epigenetic modification in human prostate LNCaP cells. Treatment with AST in human LNCaP cells reduced the methylation of 21 CpG sites of the GSTP1 CpG island but did not affect the three CpG sites of the Nrf2 promoter region. AST induced the mRNA expression and protein expression of both Nrf2 and GSTP1. It also increased the mRNA expression of NQO1 in sh-mock LNCaP cells but not in sh-SETD7 LNCaP cells. Furthermore, AST reduced the protein expression of DNMT3b and significantly inhibited DNMT and HDAC activities in vitro. Taken together, these results suggest that AST decreased the methylation status of the GSTP1, and these epigenetic modifying effects may originate from the decreasing activities of epigenetic modification enzymes, contributing to the overall beneficial health effects of AST.

NQO1-Mediated Tumor-Selective Lethality and Radiosensitization for Head and Neck Cancer

Ionizing radiation (IR) is a key therapeutic regimen for many head and neck cancers (HNC). However, the 5-year overall survival rate for locally advanced HNCs is approximately 50% and better therapeutic efficacy is needed.

NAD(P)H

quinone oxidoreductase 1 (NQO1) is overexpressed in many cancers, and β-lapachone (β-lap), a unique NQO1 bioactivatable drug, exploits this enzyme to release massive reactive oxygen species (ROS) that synergize with IR to kill by programmed necrosis. β-Lap represents a novel therapeutic opportunity in HNC leading to tumor-selective lethality that will enhance the efficacy of IR. Immunohistochemical staining and Western blot assays were used to assess the expression levels of NQO1 in HNC cells and tumors. Forty-five percent of endogenous HNCs expressed elevated NQO1 levels. In addition, multiple HNC cell lines and tumors demonstrated elevated levels of NQO1 expression and activity and were tested for anticancer lethality and radiosensitization by β-lap using long-term survival assays. The combination of nontoxic β-lap doses and IR significantly enhanced NQO1-dependent tumor cell lethality, increased ROS, TUNEL-positive cells, DNA damage, NAD(+), and ATP consumption, and resulted in significant antitumor efficacy and prolonged survival in two xenograft murine HNC models, demonstrating β-lap radiosensitization of HNCs through a NQO1-dependent mechanism. This translational study offers a potential biomarker-driven strategy using NQO1 expression to select tumors susceptible to β-lap-induced radiosensitization. Mol Cancer Ther; 15(7); 1757-67. ©2016 AACR.

Evaluation of the antibacterial and antibiofilm activities of novel CRAMP-vancomycin conjugates with diverse linkers

We report the design, synthesis and antibacterial activity analysis of conjugates of vancomycin and cathelicidin-related antimicrobial peptides (CRAMP). Vancomycin inhibits the nascent peptidoglycan synthesis and is highly active against Gram-positive bacteria, whereas Gram-negative bacteria are generally insensitive due to a protective outer membrane. CRAMP is known to translocate across the Gram-negative outer membrane by a self-promoted uptake mechanism. Vancomycin-CRAMP conjugates were synthesized using click chemistry with diverse hydrophilic and hydrophobic linkers, with CRAMP functioning as a carrier peptide for the transfer of vancomycin through the outer membrane. Small hydrophobic linkers with an aromatic group result in the most active conjugates against planktonic Gram-negative bacteria, while maintaining the high activity of vancomycin against Gram-positive bacteria. These conjugates thus show a broad-spectrum activity, which is absent in CRAMP or vancomycin alone, and which is strongly improved compared to an equimolar mixture of CRAMP and vancomycin. In addition, these conjugates also show a strong inhibitory activity against S. Typhimurium biofilm formation.

Acute toxicity of binary-metal mixtures of copper, zinc, and nickel to Pimephales promelas: Evidence of more-than-additive effect

Metal mixture toxicity has been studied for decades. However, the results are not consistent, and thus ecological risk assessment and regulation of mixtures has been difficult. The objective of the present study was to use a systematic experimental design to characterize the toxicity of binary-metal mixture of Cu, Zn, and Ni to Pimephales promelas, typically to determine whether the effect of these binary-metal mixtures on P. promelas is more-than-additive. Standard 96-h toxicity tests were conducted with larval P. promelas based on US Environmental and Protection Agency methods to determine metal mixture effects. All experiments were conducted in synthetic moderately hard water with no addition of dissolved organic matter. Three different effect analysis approaches, the MixTox model, the Finney model, and the toxic unit method, were used for comparison. The results indicate that the toxicity of Cu+Zn, Cu+Ni, and Zn+Ni mixtures to P. promelas was more-than-additive. Among the 3 mixtures, the effect of the Cu+Ni mixture was the most profound. The results of the present study are useful for applications to models such as the metal mixture biotic ligand model. More research should be conducted to determine the mechanisms of acute and chronic toxicity of metal mixtures.